Gradient descent, how neural networks learn | Chapter 2, Deep learning
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This video was supported by Amplify Partners.
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To learn more, I highly recommend the book by Michael Nielsen
The book walks through the code behind the example in these videos, which you can find here:
MNIST database:
Also check out Chris Olah’s blog:
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.
And if you like that, you’ll *love* the publications at distill:
For more videos, Welch Labs also has some great series on machine learning:
“But I’ve already voraciously consumed Nielsen’s, Olah’s and Welch’s works“, I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book “Deep Learning“ by Goodfellow, Bengio, and Courville.
Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:
Music by Vincent Rubinetti:
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Video timeline
0:00 - Introduction
0:30 - Recap
1:49 - Using training data
3:01 - Cost functions
6:55 - Gradient descent
11:18 - More on gradient vectors
12:19 - Gradient descent recap
13:01 - Analyzing the network
16:37 - Learning more
17:38 - Lisha Li interview
19:58 - Closing thoughts
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